Regulation of Angiotensin- Converting Enzyme 2 in Obesity: Implications for COVID-19

  1. Saba Al Heialy
  2. Mahmood Yaseen Hachim
  3. Abiola Senok
  4. Mellissa Gaudet
  5. Ahmad Abou Tayoun
  6. Rifat Hamoudi
  7. Alawi Alsheikh-Ali
  8. Qutayba Hamid

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Abstract

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  1. Version published to 10.3389/fphys.2020.555039
  2. ScreenIT

    SciScore for 10.1101/2020.04.17.046938: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Differentially expressed genes in bronchial epithelial cells infected with SARS-CoV-2: In order to identify essential differentially expressed genes in SARS-CoV-2 infected versus non-infected epithelial cells, we re-analyzed the publicly available transcriptomic dataset (GSE147507) recently uploaded to the Gene Expression Omnibus (GEO) (11).
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    Principle component analysis and Heatmap clustering were generated, and differentially expressed genes (DEGs) were identified using LIMMA algorithm built in AltAnalyze software.
    LIMMA
    suggested: (LIMMA, RRID:SCR_010943)
    Graphical visualization of the gene was made using the Metascape online tool for gene ontology (http://metascape.org) (13).
    Metascape
    suggested: (Metascape, RRID:SCR_016620)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.04.17.046938v1 on bioRxiv